Moving acoustic sources can be tracked by acquiring and analyzing their acoustic signals. If an array of microphones is used, the methods are typically based on beam-forming, time-delay estimation, or probabilistic modeling. With beam-forming, time-shifted signals are summed to determine source locations according to measured delays. Unfortunately, beam-forming methods are computationally complex. Time-delay estimation attempts to correlate signals to determine peaks. However, such methods are not suitable for reverberant environments. Probabilistic methods typically use Bayesian networks, M. S. Brandstein, J. E. Adcock, and H. F. Silverman, “A practical time delay estimator for localizing speech sources with a microphone array,” Computer Speech and Language, vol. 9, pp. 153-169, April 1995; S. T. Birtchfield and D. K. Gillmor, “Fast Bayesian acoustic localization,” Proceedings of the International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2002; and T. Pham and B. Sadler, “Aeroacoustic wideband array processing for detection and tracking of ground vehicles,” J. Acoust. Soc. Am. 98, No. 5, pt. 2, 2969, 1995.
One method involves ‘black box’ training of cross-spectra, G. Arslan, F. A. Sakarya, and B. L. Evans, “Speaker Localization for Far-field and Near-field Wideband Sources Using Neural Networks,” IEEE Workshop on Non-linear Signal and Image Processing, 1999. Another method models cross-sensor differences, J. Weng and K. Y. Guentchev, “Three-dimensional sound localization from a compact non-coplanar array of microphones using tree-based learning,” Journal of the Acoustic Society of America, vol. 110, no. 1, pp. 310 - 323, July 2001.
There are a number of problems with tracking moving signal sources. Typically, the signals are non-stationary due to the movement. There can also be significant time-varying multi-path interference, particularly in highly-reflective environments. It is desired to track a variety of different signal sources in different environments.